Boiler exhaust gas temperature modeling based on particle swarm algorithm and support vector machine

ZHONG Yafei; WEI Hongqi; YAO Weigang; CHEN Jingwei

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Boiler exhaust gas temperature modeling based on particle swarm algorithm and support vector machine

更新时间：2025-09-28

- Boiler exhaust gas temperature modeling based on particle swarm algorithm and support vector machine
- Thermal Power Generation Vol. 45, Issue 1, Pages: 32-36(2016)
- 作者机构：
  
  东南大学能源与环境学院
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP18;TK227
- Published：2016
- 稿件说明：
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ZHONG Yafei, WEI Hongqi, YAO Weigang, et al. Boiler exhaust gas temperature modeling based on particle swarm algorithm and support vector machine[J]. Thermal power generation, 2016, 45(1): 32-36.
DOI：

ZHONG Yafei, WEI Hongqi, YAO Weigang, et al. Boiler exhaust gas temperature modeling based on particle swarm algorithm and support vector machine[J]. Thermal power generation, 2016, 45(1): 32-36. DOI：

摘要

排烟热损失是机组锅炉热损失中最大的一项

建立锅炉排烟温度模型对锅炉运行有重要意义。为此

以某660 MW机组锅炉燃烧调整试验数据为基础

采用支持向量机建立锅炉排烟温度模型

并利用改进的混沌粒子群算法对支持向量机的相关参数进行优化

使模型的准确性显著提高。与遗传算法相比

粒子群算法无交叉和变异

算法简单

泛化能力强

有很好的实用性。基于粒子群算法和支持向量机的排烟温度模型能够很好地预测排烟温度的变化

可为锅炉的运行提供指导。

Abstract

Exhaust heat loss is the largest item in the heat loss of boiler units

which largely depends on the exhaust temperature.Therefore

the establishment of boiler exhaust gas temperature model has important significance to improving the boiler efficiency.On the basis of the combustion adjustment test data of a660 MW unit boiler

the support vector machine(SVM)method was employed to build up a boiler exhaust temperature model.Moreover

the improved chaotic particle swarm algorithm(CPSO)was applied to optimize the related parameters for the support vector machine

which greatly improved the accuracy of the model.The result shows that

compared with the genetic algorithm

the PSO algorithm has no crossover and mutation

it is simple and has high generalization ability and strong practicability.Therefore

the exhaust temperature model based on PSO algorithm and SVM can predict the change of the exhaust temperature precisely

and provide theoretical guidance for the boiler operation efficiently.

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Related Author

CHI Zuo-he

SUN Gong-gangChina Metrological College

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XIONG Yangheng2

ZHOU Hao3

Related Institution

China Institute of Metrology

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